Wireless Mobile Communication and Healthcare. Third International Conference, MobiHealth 2012, Paris, France, November 21-23, 2012, Revised Selected Papers

Research Article

Depth Limited Treatment Planning and Scheduling for Electronic Triage System in MCI

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  • @INPROCEEDINGS{10.1007/978-3-642-37893-5_26,
        author={Ayaka Kashiyama and Akira Uchiyama and Teruo Higashino},
        title={Depth Limited Treatment Planning and Scheduling for Electronic Triage System in MCI},
        proceedings={Wireless Mobile Communication and Healthcare. Third International Conference, MobiHealth 2012, Paris, France, November 21-23, 2012, Revised Selected Papers},
        proceedings_a={MOBIHEALTH},
        year={2013},
        month={4},
        keywords={Mass Casualty Incident Disaster Medical Care Treatment Planning and Scheduling NP-hard Depth Limited Search},
        doi={10.1007/978-3-642-37893-5_26}
    }
    
  • Ayaka Kashiyama
    Akira Uchiyama
    Teruo Higashino
    Year: 2013
    Depth Limited Treatment Planning and Scheduling for Electronic Triage System in MCI
    MOBIHEALTH
    Springer
    DOI: 10.1007/978-3-642-37893-5_26
Ayaka Kashiyama1,*, Akira Uchiyama1,*, Teruo Higashino1,*
  • 1: Osaka University
*Contact email: a-kasiym@ist.osaka-u.ac.jp, utiyama@ist.osaka-u.ac.jp, higashino@ist.osaka-u.ac.jp

Abstract

For supporting rescue operations in disasters, vital data collections in wireless sensor networks have been proposed so far. In such systems, we can expect to predict each patient’s probability of survival based on real-time vital data. In this paper, we focus on prehospital care and propose a method to determine treatment plans and schedules of patients. The proposed method maximizes the number of expected saved patients under limited medical resources. This optimization problem is called Treatment Planning and Scheduling, which is NP-hard. Therefore, we propose a heuristic algorithm based on depth-limited search. We have compared the proposed method with greedy methods. The results show the proposed method can derive solutions in practical time and the average number of saved patients is 10% larger compared to the greedy methods.